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Record W2798064111 · doi:10.2139/ssrn.3241413

Regulatory Schizophrenia: Mergers, Alliances, Metal-Neutral Joint Ventures and the Emergence of a Global Aviation Cartel

2018· article· en· W2798064111 on OpenAlexaff
Paul Stephen Dempsey

Bibliographic record

VenueSSRN Electronic Journal · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsMcGill University
Fundersnot available
KeywordsCartelBusinessJoint (building)AviationInternational tradeSchizophrenia (object-oriented programming)ChinaIndustrial organizationPolitical scienceEngineeringPsychologyAerospace engineeringPsychiatry

Abstract

fetched live from OpenAlex

A IRLINES ARE A HIGH FIXED-COST, safety-and labor-in- tensive industry prone to destructive competition.1 Demand is fickle, and fuel prices are volatile.In a manner consistent with Garrett Hardin's insight into the Tragedy of the Commons, 2 after deregulation, airline management has behaved in an individually rational and collectively irrational manner, competing away airline profits in order to fill seats that otherwise would fly empty.3 Destructive competition emerged from airline deregulation, 4 eventually causing every major pre-deregulation interstate

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0060.004
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0090.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.221
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2018
Admission routes1
Has abstractno

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